Open AccessArticle
Intercomparison of Different Sources of Precipitation Data in the Brazilian Legal Amazon
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Fabrício Daniel dos Santos Silva, Claudia Priscila Wanzeler da Costa, Vânia dos Santos Franco, Helber Barros Gomes, Maria Cristina Lemos da Silva, Mário Henrique Guilherme dos Santos Vanderlei, Rafaela Lisboa Costa, Rodrigo Lins da Rocha Júnior, Jório Bezerra Cabral Júnior, Jean Souza dos Reis, Rosane Barbosa Lopes Cavalcante, Renata Gonçalves Tedeschi, Naurinete de Jesus da Costa Barreto, Antônio Vasconcelos Nogueira Neto, Edmir dos Santos Jesus and Douglas Batista da Silva Ferreira
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Abstract
Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of
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Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative
bias. Parameters such as Pearson’s correlation (
r), root-mean-square error (
RMSE) and Taylor diagrams (
SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.
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